12 papers
In-Context Learning of Temporal Point Processes with Foundation Inference Models
David Berghaus, Patrick Seifner, Kostadin Cvejoski +2
Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamical rules and predict future even…
In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
Patrick Seifner, Kostadin Cvejoski, David Berghaus +2
Stochastic differential equations (SDEs) describe dynamical systems where deterministic flows, governed by a drift function, are superimposed with random fluctuations, dictated by…
Powerful Training-Free Membership Inference Against Autoregressive Language Models
David IliÄ, David StanojeviÄ, Kostadin Cvejoski
Fine-tuned language models pose significant privacy risks, as they may memorize and expose sensitive information from their training data. Membership inference attacks (MIAs) provi…
Learning the Signature of Memorization in Autoregressive Language Models
David IliÄ, Kostadin Cvejoski, David StanojeviÄ +1
All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibration), each bounded by the des…
Protecting Private Code in IDE Autocomplete using Differential Privacy
Evgeny Grigorenko, David StanojeviÄ, David IliÄ +2
Modern Integrated Development Environments (IDEs) increasingly leverage Large Language Models (LLMs) to provide advanced features like code autocomplete. While powerful, training t…
Control Models for In-IDE Code Completion
Aral de Moor, Yana Hrynevich, Hleb Badzeika +6
We introduce control models for LLM-powered code completion in JetBrains IDEs: ML classifiers which trigger inference and filter the generated suggestions to better align them with…